Regression Models and Utilities for Repeated Measures and Panel Data
Check if variables are constant or variable over time.
Convert WBFormula to list (for backward compatibility)
Asymmetric effects models fit with GEE
Estimate asymmetric effects models using first differences
Balance panel data by filling gaps
Registry of known basis functions and their reproducible attributes
Utilities for handling basis expansion functions in formulas
Conditionally add backticks based on syntax validity
Add backticks to names
Lightweight panel_data constructor
Filter out entities with too few observations
Check if any terms in a formula are matrix-returning
Evaluate a basis function on pooled data and extract attributes
Expand a basis matrix into individual columns in a data frame
Expand matrix terms into data columns
Extract the primary variable from a basis function call
Extract the function name from a formula term
Extract variables from random effects terms
Tidy methods for fdm and asym models
Estimate first differences models using GLS
Retrieve model formulas from wbm objects
Generate column names for expanded basis matrix
Get all interaction labels from WBFormula
Get mean variable name for a term
Retrieve panel_data metadata
Check if panel data has gaps
Check if WBFormula has interactions
Estimate Heise stability and reliability coefficients
Interaction configuration
Check if a function is a known basis function
Check if a term returns a matrix when evaluated
Check if panel data is properly sorted
Check if object is panel_data
Check if a variable is time-varying in WBFormula
Check if model uses within-transformation
Plot trends in longitudinal variables
Convert wide panels to long format
Generate differenced and asymmetric effects data
Create InteractionConfig from wbm() arguments
Prepare data for within-between modeling
Make model frames for panel_data objects
Number of observations used in wbm models
Internal vctrs methods
Create panel data frames
Predictions and simulations from within-between GEE models
Predictions and simulations from within-between models
Print method for WBFormula
Process a matrix term for within-between decomposition
Reconstruct a basis function call with a modified variable
Objects exported from other packages
Scan for gaps in panel data
Determine if interactions should be de-meaned
Summarize panel data frames
Remove backticks from names
Convert panel_data to regular data frame
Update parsed formula object for matrix terms
Determine if "old-style" interaction processing is needed
Create WBFormula from parser output (for migration)
WBFormula class for within-between model formula representation
Tidy methods for wbgee models
Panel regression models fit with GEE
Bayesian estimation of within-between models
Tidy methods for wbm models
Within-Between Model (wbm) class
Panel regression models fit via multilevel modeling
Convert long panel data to wide format
Provides an object type and associated tools for storing and wrangling panel data. Implements several methods for creating regression models that take advantage of the unique aspects of panel data. Among other capabilities, automates the "within-between" (also known as "between-within" and "hybrid") panel regression specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them as multilevel models (Allison, 2009 <doi:10.4135/9781412993869.d33>; Bell & Jones, 2015 <doi:10.1017/psrm.2014.7>). These models can also be estimated via generalized estimating equations (GEE; McNeish, 2019 <doi:10.1080/00273171.2019.1602504>) and Bayesian estimation is (optionally) supported via 'Stan'. Supports estimation of asymmetric effects models via first differences (Allison, 2019 <doi:10.1177/2378023119826441>) as well as a generalized linear model extension thereof using GEE.